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Preference Networks: probabilistic models for recommendation systems

Tran, Truyen, Phung, Dinh and Venkatesh, Svetha 2007, Preference Networks: probabilistic models for recommendation systems, in AusDM 2007 : Proceedings of 6th Australasian Data Mining Conference., Australian Computer Society, Gold Coast, N.S.W., pp. 195-202.

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Title Preference Networks: probabilistic models for recommendation systems
Author(s) Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name Australasian Data Mining Conference. (6th : 2007 : Gold Coast, N.S.W.)
Conference location Gold Coast, N.S.W.
Conference dates 2007/12/3 - 2007/12/4
Title of proceedings AusDM 2007 : Proceedings of 6th Australasian Data Mining Conference.
Editor(s) Christen, Peter
Kennedy, Paul
Li, Jiuyong
Kolyshkina, Inna
Williams, Graham
Publication date 2007
Start page 195
End page 202
Total pages 8
Publisher Australian Computer Society
Place of publication Gold Coast, N.S.W.
Keyword(s) Hybrid RecommenderSystems
Collaborative Filtering
Preference Networks
Conditional Markov Networks
Movie Rating
Summary Abstract
Recommender systems are important to help users select relevant and personalised information over massive amounts of data available. We propose an unified framework called Preference Network (PN) that jointly models various types of domain knowledge for the task of recommendation. The PN is a probabilistic model that systematically combines both content-based filtering and collaborative filtering into a single conditional
Markov random field. Once estimated, it serves as a probabilistic database that supports various useful queries such as rating prediction and top-N recommendation. To handle the challenging problem of learning large networks of users and items, we employ a simple but effective pseudo-likelihood with regularisation. Experiments on the movie rating data demonstrate the merits of the PN.
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2007, Australian Computer Society
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30072930

Document type: Conference Paper
Collections: Centre for Pattern Recognition and Data Analytics
Open Access Collection
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.